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Journal articles on the topic 'Whale optimizer algorithm'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (2022): 3466. http://dx.doi.org/10.3390/math10193466.

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This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is

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Ewees, Ahmed A., Zakariya Yahya Algamal, Laith Abualigah, et al. "A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators." Mathematics 10, no. 8 (2022): 1273. http://dx.doi.org/10.3390/math10081273.

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Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to b

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Wang, GuoChun, Wenyong Gui, Guoxi Liang, et al. "Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks." Complexity 2021 (August 30, 2021): 1–33. http://dx.doi.org/10.1155/2021/8130378.

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The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA

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Mehta, Pranav, Betül Sultan Yıldız, Nantiwat Pholdee, et al. "A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems." Materials Testing 65, no. 2 (2023): 210–23. http://dx.doi.org/10.1515/mt-2022-0259.

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Abstract Optimization of engineering discipline problems are quite a challenging task as they carry design parameters and various constraints. Metaheuristic algorithms can able to handle those complex problems and realize the global optimum solution for engineering problems. In this article, a novel generalized normal distribution algorithm that is integrated with elite oppositional-based learning (HGNDO-EOBL) is studied and employed to optimize the design of the eight benchmark engineering functions. Moreover, the statistical results obtained from the HGNDO-EOBL are collated with the data obt

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Euldji, Rafik, Noureddine Batel, Redha Rebhi, et al. "Optimal Backstepping-FOPID Controller Design for Wheeled Mobile Robot." Journal Européen des Systèmes Automatisés 55, no. 1 (2022): 97–107. http://dx.doi.org/10.18280/jesa.550110.

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A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable soluti

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Hemanth, Mangalapuri. "Nature inspired metaheuristic effectiveness used in phishing intrusion detection systems with grey wolf algorithm techniques." i-manager’s Journal on Future Engineering and Technology 20, no. 3 (2025): 23. https://doi.org/10.26634/jfet.20.3.21816.

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Phishing attacks pose a severe cybersecurity threat, often bypassing traditional Intrusion Detection Systems (IDS) due to high false positives and low detection accuracy. This study enhances phishing detection by integrating nature-inspired metaheuristic algorithms with machine learning. Support Vector Machine (SVM) performance is optimized using Grey Wolf Optimizer (GWO), Firefly Algorithm, Bat Algorithm, and Whale Optimization Algorithm, mimicking natural behaviours for improved efficiency. Experimental evaluation shows that our model outperforms traditional methods, achieving over 95% detec

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Baihaqi, Muhammad Aghniya, and Dana Marsetiya Utama. "No-Wait Flowshop Permutation Scheduling Problem : Fire Hawk Optimizer Vs Beluga Whale Optimization Algorithm." Jurnal Ilmiah Teknik Industri 22, no. 1 (2023): 124–36. http://dx.doi.org/10.23917/jiti.v22i1.21128.

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No-Wait Flowshop Permutation Scheduling Problem (NWPFSP) is a scheduling problem that states that every job completed on machine n must be processed immediately on the next machine. The NWPFSP problem is an extension of the flowshop problem. This article proposes two new algorithms fire hawk optimization and beluga whale optimization, to solve the NWPFSP problem and minimize makespan. The two new algorithms developed to solve the NWPFSP problem are tested on three different cases. Each algorithm was run 30 times and was compared using an independent sample t-test. The results were also compare

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Zhai, Q. H., T. Ye, M. X. Huang, S. L. Feng, and H. Li. "Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization." Computational Intelligence and Neuroscience 2020 (August 28, 2020): 1–19. http://dx.doi.org/10.1155/2020/8834162.

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In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maxi

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Hudaib, Amjad A., and Hussam N. Fakhouri. "Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic." Modern Applied Science 12, no. 1 (2017): 32. http://dx.doi.org/10.5539/mas.v12n1p32.

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Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by th

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Sheng, Long, Sen Wu, and Zongyu Lv. "Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller." Applied Sciences 15, no. 8 (2025): 4530. https://doi.org/10.3390/app15084530.

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The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared

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Long, Nguyen Ngoc, Nguyen Huu Quyet, Nguyen Xuan Tung, Bui Tien Thanh, and Tran Ngoc Hoa. "Damage Identification of Suspension Footbridge Structures using New Hunting-based Algorithms." Engineering, Technology & Applied Science Research 13, no. 4 (2023): 11085–90. http://dx.doi.org/10.48084/etasr.5983.

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Metaheuristic algorithms have been applied to tackle challenging optimization problems in various domains, such as health, education, manufacturing, and biology. In particular, the field of Structural Health Monitoring (SHM) has received significant interest, particularly in the area of damage identification in structures. Popular optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO), Artificial Hummingbird Algorithm (AHA), Moth Flame Optimizer (MFO), among others, have been employed to address

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Alkasassbeh, Abdelmajeed, Hatem H. Almasaeid, and Bilal Yasin. "Critical Failure Mode Determination of Steel Moment Frames by Plastic Analysis Optimization Principles." Buildings 13, no. 8 (2023): 2008. http://dx.doi.org/10.3390/buildings13082008.

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Determining the failure or failure mode of structures has long been a challenge for civil engineers. Traditional methods for analyzing structures are costly and complex. Plastic analysis, which involves combining pre-defined mechanisms, offers a less complex approach. However, as the number of potential mechanism combinations, or the search space, increases with the growing complexity of structural members, the effectiveness of this method diminishes. To address this issue, optimizers have been applied in the field of structural engineering to efficiently solve problems with large search space

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Manish, Chhabra Rajesh E. "Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1195–207. https://doi.org/10.11591/ijeecs.v38.i2.pp1195-1207.

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This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-det

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Singh, Narinder, and Hanaa Hachimi. "A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization." Mathematical and Computational Applications 23, no. 1 (2018): 14. http://dx.doi.org/10.3390/mca23010014.

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Tian, Fengqing, Donghua Zhang, Ying Yuan, Guangchun Fu, Xiaomin Li, and Guanghua Chen. "Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer." Symmetry 15, no. 12 (2023): 2206. http://dx.doi.org/10.3390/sym15122206.

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Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-ho

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Chhabra, Manish, and Rajesh E. "Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1195. https://doi.org/10.11591/ijeecs.v38.i2.pp1195-1207.

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<p>This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-deterministi

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Hussien, Abdelazim G., Diego Oliva, Essam H. Houssein, Angel A. Juan, and Xu Yu. "Binary Whale Optimization Algorithm for Dimensionality Reduction." Mathematics 8, no. 10 (2020): 1821. http://dx.doi.org/10.3390/math8101821.

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Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the da

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Seçkiner, Serap Ulusam, and Şeyma Yilkici Yüzügüldü. "A new health-based metaheuristic algorithm: cholesterol algorithm." International Journal of Industrial Optimization 4, no. 2 (2023): 115–30. http://dx.doi.org/10.12928/ijio.v4i2.7651.

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This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Se

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Sun, Shuming, Yijun Chen, and Ligang Dong. "An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm." Mathematical Biosciences and Engineering 21, no. 2 (2024): 2787–812. http://dx.doi.org/10.3934/mbe.2024124.

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<abstract> <p>In response to the problem of coverage redundancy and coverage holes caused by the random deployment of nodes in wireless sensor networks (WSN), a WSN coverage optimization method called GARWOA is proposed, which combines the genetic algorithm (GA) and reinforced whale optimization algorithm (RWOA) to balance global search and local development performance. First, the population is initialized using sine map and piecewise linear chaotic map (SPM) to distribute it more evenly in the search space. Secondly, a non-linear improvement is made to the linear control factor '

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El-Dabah, Mahmoud, Mohamed A. Ebrahim, Ragab A. El-Sehiemy, Z. Alaas, and M. M. Ramadan. "A Modified Whale Optimizer for Single- and Multi-Objective OPF Frameworks." Energies 15, no. 7 (2022): 2378. http://dx.doi.org/10.3390/en15072378.

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This paper is concerned with an imperative operational problem, called the optimal power flow (OPF), which has several technical and economic points of view with respect the environmental concerns. This paper proposes a multiple-objective optimizer NSWOA (non-dominated sorting whale optimization algorithm) for resolving single-objective OPFs, as well as multi-objective frameworks. With a variety of technical and economic power system objectives, the OPF can be formulated. These objectives are treated as single- and multi-objective OPF issues that are deployed with the aid of the proposed NSWOA

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Dehghani, Mohammad, Štěpán Hubálovský, and Pavel Trojovský. "Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm." Sensors 21, no. 15 (2021): 5214. http://dx.doi.org/10.3390/s21155214.

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Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed C

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Qin, Shujin, Jiaxin Wang, Jiacun Wang, Xiwang Guo, Liang Qi, and Yaping Fu. "Linear Disassembly Line Balancing Problem with Tool Deterioration and Solution by Discrete Migratory Bird Optimizer." Mathematics 12, no. 2 (2024): 342. http://dx.doi.org/10.3390/math12020342.

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In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assig

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Safaeian, Mojgan, Amir M. Fathollahi-Fard, Kamyar Kabirifar, Maziar Yazdani, and Mohammad Shapouri. "Selecting Appropriate Risk Response Strategies Considering Utility Function and Budget Constraints: A Case Study of a Construction Company in Iran." Buildings 12, no. 2 (2022): 98. http://dx.doi.org/10.3390/buildings12020098.

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Successful implementation of construction projects worldwide calls for a set of effective risk management plans in which uncertainties associated with risks and effective response strategies are addressed meticulously. Thus, this study aims to provide an optimization approach with which risk response strategies that maximize the utility function are selected. This selection is by opting for the most appropriate strategies with the highest impact on the project regarding the weight of each risk and budget constraints. Moreover, the risk assessment and response strategy of a construction project

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Chiu, Chien-Ching, Po-Hsiang Chen, Wei Chien, Eng Hock Lim, and Guo-Zheng Chen. "Microwave Imaging for Half-Space Conductors Using the Whale Optimization Algorithm and the Spotted Hyena Optimizer." Applied Sciences 13, no. 10 (2023): 5857. http://dx.doi.org/10.3390/app13105857.

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This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in one half-space. The scattered field measured outside the conductor surface with the boundary condition is used to reconstruct the object using the WOA and SHO algorithms. Several scenarios of reconstruction accuracy were compared for the WOA and SHO. The numerical simulations prove that the WOA has a better rec

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Ahmed, A. Elbaset, A. Mohamed AboulFotouh, Abou El-Zahab Essam, and A. Moustafa Hassan M. "Recloser-fuse settings in distribution systems with optimizing multiple distributed generation considering technical aspects." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1135–49. https://doi.org/10.11591/ijeecs.v17.i3.pp1135-1149.

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With the widespread of using distributed generation, the connection of DGs in the distribution system causes miscoordination between protective devices. This paper introduces the problems associated with recloser fuse miscoordination (RFM) in the presence of single and multiple DG in a radial distribution system. Two Multi objective optimization problems are presented. The first is based on technical impacts to determine the optimal size and location of DG considering system power loss reduction and enhancement the voltage profile with a certain constraints and the second is used for minimizin

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Fathy, Ahmed, Hegazy Rezk, Dalia Yousri, Essam H. Houssein, and Rania M. Ghoniem. "Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization." Mathematics 9, no. 22 (2021): 2971. http://dx.doi.org/10.3390/math9222971.

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Thermoelectric generation systems (TEGSs) are used to convert temperature difference and heat flow into DC power based on the Seebeck theorem. The basic unit of TEGS is the thermoelectric module (TEM). TEGSs have gained increasing interest in the research fields of sustainable energy. The output power from TEM is mostly reliant on differential temperature between the hot and cold sides of the TEM added to the value of the load. As such, a robust MPPT strategy (MPPTS) is required to ensure that the TEGS is operating near to the MPP while varying the operating conditions. Two main drawbacks may

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Yab, Li Yu, Noorhaniza Wahid, and Rahayu A Hamid. "Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study." JOIV : International Journal on Informatics Visualization 7, no. 2 (2023): 477. http://dx.doi.org/10.30630/joiv.7.2.1509.

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Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more se

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Korashy, Ahmed, Salah Kamel, Francisco Jurado, and Abdel-Raheem Youssef. "Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm for Optimal Coordination of Direction Overcurrent Relays." Electric Power Components and Systems 47, no. 6-7 (2019): 644–58. http://dx.doi.org/10.1080/15325008.2019.1602687.

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Achouri, Mourad, Youcef Zennir, and Cherif Tolba. "Fuzzy-PI Conroller Tuned With GWO, WOA And TLBO For 2 DOF Robot Trajectory Control." Algerian Journal of Signals and Systems 7, no. 1 (2022): 1–6. http://dx.doi.org/10.51485/ajss.v7i1.150.

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In this study, a manipulator robot with two degrees of freedom was controlled by Fuzzy-PI adjust by three meta-heuristic algorithms (Grey wolf optimizer (GWO), Whale Optimization Algorithm (WOA) and Teaching–learning-based optimization (TLBO)). The scale factors of the fuzzy system of the takagi-soguno type (the width of the membership functions) and the parameters of PI were optimized by those three algorithms under the cost function of the absolute magnitude of the mean error (MAE). In order to investigate the robustness of the proposed controller we considered the friction forces. The resul

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Dehghani, Mohammad, Zeinab Montazeri, and Štěpán Hubálovský. "GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems." Mathematics 9, no. 11 (2021): 1190. http://dx.doi.org/10.3390/math9111190.

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There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimiz

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Loucif, Fatiha, and Sihem Kechida. "Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms." Open Computer Science 10, no. 1 (2020): 396–407. http://dx.doi.org/10.1515/comp-2020-0144.

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AbstractIn this paper, a sliding mode controller (SMC) with PID surface is designed for the trajectory tracking control of a robot manipulator using different optimization algorithms such as, Antlion Optimization Algorithm (ALO) Sine Cosine Algorithm (SCA) Grey Wolf Optimizer (GWO) and Whale Optimizer Algorithm (WOA). The aim of this work is to introduce a novel SMC-PID-ALO to control nonlinear systems, especially the position of two of the joints of a 2DOF robot manipulator. The basic idea is to determinate four optimal parameters (Kp, Ki, Kd and lamda) ensuring the best performance of a robo

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Mir, Imran, Faiza Gul, Suleman Mir, et al. "Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective." Biomimetics 8, no. 3 (2023): 294. http://dx.doi.org/10.3390/biomimetics8030294.

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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to fu

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Sadeghi, Ali, Sajjad Amiri Doumari, Mohammad Dehghani, Zeinab Montazeri, Pavel Trojovský, and Hamid Jafarabadi Ashtiani. "A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems." Applied Sciences 11, no. 10 (2021): 4382. http://dx.doi.org/10.3390/app11104382.

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Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups

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Randa, Jalaa Yahya, and Hadi Abbas Nizar. "Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 293–302. https://doi.org/10.11591/ijece.v12i1.pp293-302.

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A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluate

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Andic, Cenk, Sercan Ozumcan, Metin Varan, and Ali Ozturk. "A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units." International Journal of Robotics and Control Systems 4, no. 2 (2024): 606–27. http://dx.doi.org/10.31763/ijrcs.v4i2.1341.

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This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic sy

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Y. Hamed, Ahmed, M. Kh Elnahary, and Hamdy H. El-Sayed. "Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm." International Journal of Advanced Networking and Applications 14, no. 06 (2023): 5686–95. http://dx.doi.org/10.35444/ijana.2023.14605.

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Cloud computing is now dominant in high-performance distributed computing, offering resource polling and ondemand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the

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Liu, Yixing, Shaowen Yang, Dongjie Li, and Shouming Zhang. "Improved Whale Optimization Algorithm for Solving Microgrid Operations Planning Problems." Symmetry 15, no. 1 (2022): 36. http://dx.doi.org/10.3390/sym15010036.

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Microgrid operations planning is one of the keys to ensuring the safe and efficient outputs of distributed energy resources (DERs) and the stable operation of a power system in a microgrid (MG). In this study, for the symmetry in renewable energy and microgrid systems, and coordinated control based on a storage battery system, an MG dispatching model with DER conditions and integrated costs is established in grid-connected mode, on the basis of MG operation costs, interaction costs, and pollutant emissions costs. Moreover, an optimization objective for minimizing integrated costs is establishe

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Boursianis, Achilles D., Maria S. Papadopoulou, Marco Salucci, et al. "Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers." Applied Sciences 11, no. 18 (2021): 8330. http://dx.doi.org/10.3390/app11188330.

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Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear a

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Xu, Tao, and Chaoyue Chen. "DBO-AWOA: An Adaptive Whale Optimization Algorithm for Global Optimization and UAV 3D Path Planning." Sensors 25, no. 7 (2025): 2336. https://doi.org/10.3390/s25072336.

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The rapid expansion of unmanned aerial vehicle (UAV) applications in complex environments presents significant challenges in 3D path planning, particularly in overcoming the limitations of traditional methods for dynamic obstacle avoidance and computational efficiency. To address these challenges, this study introduces an adaptive whale optimization algorithm (DBO-AWOA), which incorporates chaotic mapping, nonlinear convergence factors, adaptive inertia mechanisms, and dung beetle optimizer-inspired reproductive behaviors. Specifically, the algorithm utilizes ICMIC chaotic mapping to enhance i

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Abbassi, Rabeh, Salem Saidi, Shabana Urooj, Bilal Naji Alhasnawi, Mohamad A. Alawad, and Manoharan Premkumar. "An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models." Mathematics 11, no. 22 (2023): 4565. http://dx.doi.org/10.3390/math11224565.

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Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The

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Aribowo, Widi, Bambang Suprianto, and Aditya Prapanca. "A novel modified dandelion optimizer with application in power system stabilizer." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 2034–41. https://doi.org/10.11591/ijai.v12.i4.pp2034-2041.

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This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the whale opti

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Hussien, Abdelazim G., Fatma A. Hashim, Raneem Qaddoura, Laith Abualigah, and Adrian Pop. "An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization." Processes 10, no. 11 (2022): 2254. http://dx.doi.org/10.3390/pr10112254.

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Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle algorithm (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in the sub-optimal region and have a slow convergence, especially in high-dimensional tasks problems. This paper suggests an enhanced ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as an internal operator in the updating process. ErWCA also uses a control-randomization operator. To verify this version, a comparison between EErWCA and other algorithms, namely, classical ErWCA, water cy

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Liang, Qingwei, Shu-Chuan Chu, Qingyong Yang, Anhui Liang, and Jeng-Shyang Pan. "Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks." Sensors 22, no. 11 (2022): 4275. http://dx.doi.org/10.3390/s22114275.

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The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communic

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Boghdady, Tarek A., Samar G. A. Nasser, and Essam El-Din Aboul Zahab. "Energy harvesting maximization by integration of distributed generation based on economic benefits." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 610–25. https://doi.org/10.11591/ijeecs.v25.i2.pp610-625.

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The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering th

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Ababneh, Jafar. "A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–14. http://dx.doi.org/10.1155/2021/3517145.

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In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whal

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Ferahtia, Seydali, Azeddine Houari, Mohamed Machmoum, Mourad Ait-Ahmed, and Abdelhakim Saim. "Optimal Control Strategy for Floating Offshore Wind Turbines Based on Grey Wolf Optimizer." Applied Sciences 13, no. 20 (2023): 11595. http://dx.doi.org/10.3390/app132011595.

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Due to the present trend in the wind industry to operate in deep seas, floating offshore wind turbines (FOWTs) are an area of study that is expanding. FOWT platforms cause increased structural movement, which can reduce the turbine’s power production and increase structural stress. New FOWT control strategies are now required as a result. The gain-scheduled proportional-integral (GSPI) controller, one of the most used control strategies, modifies the pitch angle of the blades in the above-rated zone. However, this method necessitates considerable mathematical approximations to calculate the co

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Aribowo, Widi, Bambang Suprianto, and Aditya Prapanca. "A novel modified dandelion optimizer with application in power system stabilizer." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 2033. http://dx.doi.org/10.11591/ijai.v12.i4.pp2033-2041.

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<p>This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the w

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Karthikeyan, C., E. Sreedevi, Naveen Kumar, E. Vamsidhar, T. Rajesh Kumar, and D. Vijendra Babu. "Cost Optimization in Neural Network using Whale Swarm Algorithm with Batched Gradient Descent Optimizer." IOP Conference Series: Materials Science and Engineering 993 (December 31, 2020): 012047. http://dx.doi.org/10.1088/1757-899x/993/1/012047.

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Peng, Yao, and Yang Chen. "Integrative soft computing approaches for optimizing thermal energy performance in residential buildings." PLOS ONE 18, no. 9 (2023): e0290719. http://dx.doi.org/10.1371/journal.pone.0290719.

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As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based invest

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Rehman, Bilal ur, Mohammad Inayatullah Babar, Arbab Waheed Ahmad, et al. "Joint user grouping and power control using whale optimization algorithm for NOMA uplink systems." PeerJ Computer Science 8 (March 11, 2022): e882. http://dx.doi.org/10.7717/peerj-cs.882.

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The non-orthogonal multiple access (NOMA) scheme has proven to be a potential candidate to enhance spectral potency and massive connectivity for 5G wireless networks. To achieve effective system performance, user grouping, power control, and decoding order are considered to be fundamental factors. In this regard, a joint combinatorial problem consisting of user grouping and power control is considered, to obtain high spectral-efficiency for NOMA uplink system with lower computational complexity. To solve the joint problem of power control and user grouping, for Uplink NOMA, we have used a newl

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